EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking

Synergistic inhibition of glioblastoma multiforme through an in-silico analysis of luteolin and ferulic acid derived from Angelica sinensis and Cannabis sativa: Advancements in computational therapeutics

The primary objective of this study is to uncover novel therapeutic agents for the treatment of Glioblastoma Multiforme (GBM), a highly aggressive form of brain cancer, and Alzheimer's Disease (AD). Given the complexity and resistance associated with both conditions, the study underscores the imperative need for therapeutic alternatives that can traverse the biological intricacies inherent in both neuro-oncological and neurodegenerative disorders. To achieve this, a meticulous, target-based virtual screening was employed on an ensemble of 50 flavonoids and polyphenol derivatives primarily derived from plant sources. The screening focused predominantly on molecular targets pertinent to GBM but also evaluated the potential overlap with neural pathways involved in AD. The study utilized molecular docking and Molecular Dynamic (MD) simulation techniques to analyze the interaction of these compounds with a key biological target, protein tyrosine phosphatase receptor-type Z (PTPRZ). Out of the 50 compounds examined, 10 met our stringent criteria for binding affinity and specificity. Subsequently, the highest value of binding energy was observed for the synergistic binding of luteolin and ferulic acid with the value of -10.5 kcal/mol. Both compounds exhibited inherent neuroprotective properties and demonstrated significant potential as pathway inhibitors in GBM as well as molecular modulators in AD. Drawing upon advanced in-silico cytotoxicity predictions and sophisticated molecular modeling techniques, this study casts a spotlight on the therapeutic capabilities of polyphenols against GBM. Furthermore, our findings suggest that leveraging these compounds could catalyze a much-needed paradigm shift towards more integrative therapeutic approaches that span the breadth of both neuro-oncology and neurodegenerative diseases. The identification of cross-therapeutic potential in flavonoids and polyphenols could drastically broaden the scope of treatment modalities against both fatal diseases.

Malonyl-CoA is a conserved endogenous ATP-competitive mTORC1 inhibitor

Cell growth is regulated by the mammalian/mechanistic target of rapamycin complex 1 (mTORC1), which functions both as a nutrient sensor and a master controller of virtually all biosynthetic pathways. This ensures that cells are metabolically active only when conditions are optimal for growth. Notably, although mTORC1 is known to regulate fatty acid biosynthesis, how and whether the cellular lipid biosynthetic capacity signals back to fine-tune mTORC1 activity remains poorly understood. Here we show that mTORC1 senses the capacity of a cell to synthesise fatty acids by detecting the levels of malonyl-CoA, an intermediate of this biosynthetic pathway. We find that, in both yeast and mammalian cells, this regulation is direct, with malonyl-CoA binding to the mTOR catalytic pocket and acting as a specific ATP-competitive inhibitor. When fatty acid synthase (FASN) is downregulated/inhibited, elevated malonyl-CoA levels are channelled to proximal mTOR molecules that form direct protein-protein interactions with acetyl-CoA carboxylase 1 (ACC1) and FASN. Our findings represent a conserved and unique homeostatic mechanism whereby impaired fatty acid biogenesis leads to reduced mTORC1 activity to coordinately link this metabolic pathway to the overall cellular biosynthetic output. Moreover, they reveal the existence of a physiological metabolite that directly inhibits the activity of a signalling kinase in mammalian cells by competing with ATP for binding.

Entropy-based lamarckian quantum-behaved particle swarm optimization for flexible ligand docking

AutoDock is a widely used software for flexible ligand docking problems since it is open source and easy to be implemented. In this paper, a novel hybrid algorithm is proposed and applied in the docking environment of AutoDock version 4.2.6 in order to enhance the accuracy and the efficiency for dockings with flexible ligands. This search algorithm, called entropy-based Lamarckian quantum-behaved particle swarm optimization (ELQPSO), is a combination of the QPSO with an entropy-based update strategy and the Solis and Wet local search (SWLS) method. By using the PDBbind core set v.2016, the ELQPSO is compared with the Lamarckian genetic algorithm (LGA), Lamarckian particle swarm optimization (LPSO) and Lamarckian QPSO (LQPSO). The experimental results reveal that the corresponding docking program of ELQPSO, named as EQDOCK in this paper, has a competitive performance in dealing with the protein-ligand docking problems. Moreover, for the test cases with different number of torsions, the EQDOCK outperforms the other three docking programs in finding docking conformations with small root mean squared deviation (RMSD) values in most cases. In particular, it has an advantage of solving highly flexible ligand docking problems over the others.

Monastrol derivatives: in silico and in vitro cytotoxicity assessments

Background and purpose

Cancer is the leading cause of death in today's world, therefore the efforts to achieve anticancer drugs with higher potency and fewer side effects have always been conducted by researchers in the field of pharmaceutical chemistry.Monastrol, a cytotoxic small molecule, from dihydropyrimidinone scaffold, is an inhibitor of the kinesin-5 protein. So, efforts to identify more derivatives of this molecule have been of interest.

Experimental approach

Some of monastrol's analogs as Eg5 inhibitors with different substitution patterns were analyzed, synthesized, and their cytotoxic effects were evaluated on MCF-7 and HeLa cancerous cells in vitro using the MTT assay. The structure-activity relationship (SAR) was studied in silico by molecular docking.

Findings / Results

Among all proposed structures, in ducking study, those with hydrophobic moieties on the C2-N3 region, those with a hydroxyl group on the phenyl on C4 position, and those with a carboxylic group on C5 were the best candidates. In vitro studies, on the other side, emphasized that monastrol still was the most potent derivative. Another finding was the more moderate activity of synthesized compounds on the HeLa cell compared to the MCF-7 cell line. During different challenges for substitution at 5-position, some earlier reports around the dihydropyrimidinone reactions were questioned. It seems that the change at the position 5 is not merely accessible, as earlier reports claimed. Also, we could not achieve any better cell cytotoxicity by the larger group in the thiourea region or position 5; nonetheless, it seems that the introduction of a methylene group at this position could be beneficial.

Conclusion and implications

The initial results of this study were valuable in terms of design and synthesis and will be useful for future investigations.

HIGA: A Running History Information Guided Genetic Algorithm for Protein-Ligand Docking

Protein-ligand docking is an essential part of computer-aided drug design, and it identifies the binding patterns of proteins and ligands by computer simulation. Though Lamarckian genetic algorithm (LGA) has demonstrated excellent performance in terms of protein-ligand docking problems, it can not memorize the history information that it has accessed, rendering it effort-consuming to discover some promising solutions. This article illustrates a novel optimization algorithm (HIGA), which is based on LGA for solving the protein-ligand docking problems with an aim to overcome the drawback mentioned above. A running history information guided model, which includes CE crossover, ED mutation, and BSP tree, is applied in the method. The novel algorithm is more efficient to find the lowest energy of protein-ligand docking. We evaluate the performance of HIGA in comparison with GA, LGA, EDGA, CEPGA, SODOCK, and ABC, the results of which indicate that HIGA outperforms other search algorithms.